Abstract 454: TumorMapr™ analytical software platform: Unbiased spatial analytics and explainable AI (xAI) platform for generating data, extracting information, and creating knowledge from multi to hyperplexed fluorescence and/or mass spectrometry image datasets
Authors: Samantha Panakkal, Brian Falkenstein, Akif Burak Tosun, Bruce Campbell, Michael Becich, Jeffrey Fine, D. Lansing Taylor, S. Chakra Chennubhotla, Filippo Pullara
Journal: Cancer Research (2022)

Abstract Background: The current computational analyses of multi to hyperplexed fluorescence and/or mass spectrometry image datasets from patient pathology samples are not powerful enough to extract the maximum amount of information or to create the detailed knowledge that is required to advance precision medicine in pathology, including the development of personalized therapeutic strategies, identification of potential novel targets for drug discovery, selection of optimal patient cohorts for clinical trials, and improvement of the predictive power of prognostics/diagnostics. Methods: TumorMapr harnesses the computational power of proprietary, unbiased spatial analytics, spatial systems pathology, and explainable artificial intelligence (xAI) to extract information and to create knowledge from patient primary disease pathology samples imaged on any of the existing fluorescence and/or mass spectrometry imaging platforms. Results: To demonstrate the generalizability and utility of the TumorMapr platform, we apply it on two different datasets: hyperplexed immunofluorescence-based colorectal cancer data (51 biomarkers, 431 patients) and imaging mass cytometry-based breast cancer data (35 biomarkers, 281 patients). The TumorMapr platform (i) unlike the biased intensity thresholding approaches, the unbiased and automated functional cell phenotyping discovers a continuum of cell types and cell states, including transitional, multi-transitional cell states and fusion cell types that are critical for disease progression; (ii) derives microdomains with tumor promoting and tumor suppressing properties that are highly predictive of disease progression and response to therapy; (iii) spatial systems pathology analysis taps into the current network biology knowledge databases to derive pathway interactions and signaling networks, identify novel biomarkers and potential molecular targets and drugs, in the spatial context of each microdomain; (iv) xAI application guide, for example, in the case of predicting 5-year risk of recurrence in CRC patients, provides explanations in the form of microdomain-specific networks that are driving disease progression. Using the TumorMapr pipeline we created a prognostic test that shows a vastly superior performance over current approaches in predicting 5-yr risk of recurrence in CRC patients. Further, the TumorMapr platform enables building a rich outcome-specific library of microdomains to directly apply on prospective tissue samples for a companion diagnostic test that predicts disease outcomes. Citation Format: Samantha Panakkal, Brian Falkenstein, Akif Burak Tosun, Bruce Campbell, Michael Becich, Jeffrey Fine, D. Lansing Taylor, S. Chakra Chennubhotla, Filippo Pullara. TumorMapr™ analytical software platform: Unbiased spatial analytics and explainable AI (xAI) platform for generating data, extracting information, and creating knowledge from multi to hyperplexed fluorescence and/or mass spectrometry image datasets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 454.